How machine learning can help with sustainable development and biological conservation?
There are a variety of aspects where AI can help for the public good. Future studies of computational methods can contribute to a sustainable management ecosystem by its data acquisition, interpretation, integration and model fitting.
Prof. Tom Dietterich is a leader in combining computer science and ecological sciences to build the new discipline of Ecosystem Informatics which studies methods for collecting, analyzing and visualizing data on the structure and function of ecosystems.
His group is involved in many aspects of the ecosystem, such as:

Models that can predict species distribution and their presence/absence elsewhere in order to create species distribution and migration/dispersal maps (such as DataONE Datanet, eBird project, BirdCast) .
Bio-economic models require solving large Spatio-temporal optimization problems under uncertainty.
Ecosystem prediction problems require integrating heterogeneous data sources.
Algorithms for deployment (sensor placement), cleaning and analysis of sensor network data of resulting data to increase agricultural productivity (Project TAHMO), like deployment of 20,000 hydro-meteorological stations in Africa (e.g. computational problem where to place it).
Systems for capturing, imaging, and sorting bugs combined with general image processing/machine learning/pattern recognition tools for counting and classifying them (BugID project). The goal is to develop algorithms for automating biodiversity based on visual pattern recognition by using the computer vision method.

For further information about this work, check the following resources, see:

(video) &quot;Challenges for Machine Learning in Computational Sustainability&quot; (CRCS) (2013)
(slides) Tom Dietterich, Understanding and Managing Ecosystems through AI (2013)
(study) Adapting environmental management to uncertain but inevitable change (2015)